Job Description
We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, we may help to find the right ones.
Currently, we are looking for a AI Agent Architect for Elva.
Location: Remote
Job Type: Full-Time
Overview:
We are building an AI-driven video production system designed as an intelligent multi-agent orchestration layer capable of transforming raw ideas and references into fully structured video content.
The system operates as a black-box creative engine for different types of users:
professional video creators
casual users
creators of short narrative concepts
Users provide an idea, references, or media fragments, and the system automatically orchestrates multiple AI agents responsible for analysis, scripting, editing, and production of video content.
We are looking for an engineer who can design and implement multi-agent pipelines, orchestrate AI tools, and build intelligent workflows that combine video analysis, storytelling logic, and automated editing.
This role sits at the intersection of AI systems architecture, creative tooling, and multimodal content generation.
Responsibilities:
AI Agent Architecture
Design and implement the architecture of a multi-agent video editing system including agents responsible for:
video analysis
narrative generation
editing orchestration
production and output synthesis
Define system prompts, behavioral rules, and structured instructions for agents interacting within the pipeline.
Pipeline Orchestration (n8n)
Develop and maintain complex orchestration pipelines in n8n, including:
multi-agent workflows
tool-calling logic
dynamic routing between tools and models
context passing between agents
Pipelines must be capable of selecting the most appropriate models, tools, and strategies depending on the task.
Multimodal Data Processing
Design robust pipelines for handling:
video materials
image assets
user text prompts
structured metadata
Ensure proper data transformation and context transfer across the pipeline stages.
Tool & API Integration
Integrate both external and internal APIs for multimodal generation and processing, including:
image generation
video generation
speech synthesis
audio generation
video processing services
Rapidly evaluate available APIs and select the best quality tools and models for each task.
Model Orchestration & Optimization
Tune and optimize model interactions, primarily based on Gemini models, including:
prompt engineering
structured outputs
tool-calling workflows
agent collaboration logic
Optimize pipelines for quality, reliability, and execution efficiency.
Future Architecture (RAG & Knowledge Systems)
Design systems that support:
vector databases
retrieval-augmented generation (RAG)
memory and contextual reasoning between agents
Expected Outcomes:
The pipeline system should be capable of:
Taking an idea + references as input
Analyzing the content
Generating a coherent narrative structure
Selecting appropriate visual and audio elements
Producing a high-quality, structured video output
Requirements:
AI Systems & Agent Architecture
Strong experience building multi-agent systems, including:
intent and sub-intent modeling
agent orchestration
agent communication and transport layers
summarization pipelines
context passing between agents
Workflow Orchestration
Hands-on experience with:
n8n
Agent tool-calling
n8n MCP
Experience designing complex automation pipelines is essential.
Programming
Strong practical coding skills with vibe coding mindset:
Primary languages:
Python
JavaScript
Bonus experience:
ComfyUI custom nodes
lightweight APIs (e.g., HuggingFace Spaces or inference endpoints)
Multimodal Tooling Knowledge
Ability to quickly navigate API documentation and integrate tools for:
image generation
video generation
speech synthesis
audio generation
multimodal analysis
You should know where and how to obtain the best generation quality for each modality.
Creative Thinking
Strong sense of visual rhythm and composition
Creative intuition and storytelling awareness
Good taste in video structure and montage
Ability to evaluate AI-generated output not only by metrics, but also by creative quality and narrative coherence
Video & Media Processing
Preferred experience with:
FFmpeg
video processing pipelines
image processing workflows
Nice to Have:
The following qualifications are not mandatory, but will significantly strengthen a candidate’s profile during the evaluation process:
Agentic Frameworks
Deep understanding of frameworks such as LangGraph and LangChain for building complex cyclic state graphs and stateful agent systems.
Advanced RAG & Memory Management
Experience designing long-term memory systems for agents, including mechanisms for storing and retrieving successful execution patterns or scenarios to improve future performance through experience-based retrieval.
Self-Correction & Reflection
Experience building agents with feedback loops such as self-reflection, self-critique, or strategy correction, enabling them to verify their own actions and adapt execution logic dynamically.
Evaluation & Observability
Experience with tools for monitoring, debugging, and evaluating agent chains and prompt behavior, such as LangSmith, Arize Phoenix, or Promptfoo, with the ability to identify logical failures, quality regressions, and orchestration bottlenecks.
Ideal Candidate:
The ideal candidate is someone who naturally operates between engineering and creative production, capable of building systems that automate complex media workflows while preserving narrative coherence and aesthetic quality.
You enjoy designing systems where AI agents collaborate to produce meaningful creative outputs.
What’s next?
If this role sounds like a fit — we’d love to hear from you! Just send over your CV and anything else you’d like us to consider.
We’ll review everything within five working days, and if your background matches what we’re looking for, we’ll get in touch to set up a call and get to know each other better.
